Reliability
Error budgets + gated deploys
High availability and fast recovery, with health checks and rollback paths baked into deploys.
Production machine learning that stays up and stays affordable.
Reliable production ML. Deploy faster, keep systems highly available with fast recovery, and prevent infra burn.
LLM latency
Sub-second paths
Budgeted to user value, multi-model routing
Reliability
High availability
Error budgets + gated deploys
Cost control
Right-sized inference
Batching, caching, quantisation
Proven production results
Real systems serving real users: high availability and fast recovery, sub-second user-facing paths, and inference cost kept proportional to product value. Every engagement tracks reliability, latency, and unit cost together.
Latency
Sub-second production paths
Traffic-aware batching, caching, and routing to keep user-facing experiences sharp.
Cost
Spend tracked per feature
Inference cost kept proportional to product value so infra scales without surprise.
What I Deliver
End-to-end production ML systems: from architecture and deployment to the operational playbooks that keep them healthy.
Serving & orchestration
Routing, autoscaling, batching, and multi-model strategies that balance latency and spend.
Cost guardrails
Quantisation, cache design, and capacity planning so GPUs stay controlled while performance holds.
Observability that matters
User-centric SLOs, tracing, drift detection, and alerting tuned to real incidents - not dashboards for show.
Safe experimentation
Shadow traffic, eval pipelines, and A/B controls to ship model changes without surprises.
Data pathways
Feature stores, streaming ingestion, and freshness guarantees that keep models honest in production.
Team enablement
Runbooks, on-call design, and coaching so the system stays healthy after handoff.
How I Operate
A structured, low-drama path from prototype to resilient production deployment.
1
Assess & align
Map current systems, risks, and business goals. Set measurable SLOs so success is obvious.
2
Stabilise
Introduce guardrails, tracing, and rollback paths. Fix the sharp edges that cause late-night pages.
3
Scale with guardrails
Batching, quantisation, caching, and capacity plans tailored to traffic patterns and cost targets.
4
Prove & hand off
A/B or shadow deployments, documentation, and team training so reliability lasts beyond the engagement.
Production ML System Pipeline
Data Ingestion
Validation, versioning, schema enforcement
Model Serving
Multi-model routing, batching, caching, quantization
Monitoring
Latency tracking, error budgets, drift detection, traces
Rollback/Recovery
Automated rollbacks, canary deploys, health checks
What Clients Say
Trusted by teams shipping AI where reliability, latency, and cost discipline all matter at the same time.
Asher brings calm to tense moments, turning uncertainty into clear, practical next steps. He spots risks early, explains priorities in plain language, and keeps teams steady when pressure rises.
Get in Touch
Best reached via LinkedIn. Responses typically within 48 hours for qualified inquiries.